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Oral cancer becomes the most disastrous ailment that affects the oral cavity parts of the mouth. Oral cancer diagnosis is the main challenge in the medical field. It becomes expensive and less capable of classifying oral cancer. In some cases, it may cause unnecessary morbidity and mortality. Recently, the detection of malignant and premalignant oral lesions has been a critical process owing to their low image resolution and lower acquisition time. Thus, a novel hybrid deep learning with meta-heuristic-based optimization is proposed. The pre-processing occurs by median filtering and Contrast Limited Adaptive Histogram Equalization (CLAHE). The CLAHE method is utilized to reduce unwanted noise. Finally, the classification is done by a proposed hybrid-based deep learning model termed as Recurrent Deep Belief Network (RDBN), in which the Deep Belief Network (DBN) is incorporated with the Recurrent Neural Network (RNN). Here, the RDBN helps to increase the performance classification. Furthermore, the hyperparameters of the RDBN model, such as learning rate, epochs and hidden neurons, are tuned using the proposed Hybrid Beetle-Barnacle Swarm Optimization (HBBSO) algorithm, where the Barnacles Mating Optimizer (BMO) is superimposed with Beetle Swarm Optimization (BSO) algorithms. In the given proposed model, the selected features are extracted by the optimization algorithms. Here, the parameters are fine-tuned to get the better optimal solution. From the experimental outcome, the developed model has acquired 5.2% better than PSO-RDBN, 5.6% improved than GWO-RDBN, 3.2% enhanced than BSO-RDBN and 3.5% superior to BMO-RDBN regarding accuracy. Thus, the proposed model achieves higher results for detecting oral cancer with enhanced classification performance than the existing approaches.

Today, radiologists observe a mammogram to determine whether breast tissue is normal. However, calcifications on the mammogram are so small that sometimes radiologists cannot locate them without a magnified observation to make a judgment. If clusters formed by malignant calcifications are found, the patient should undergo a needle localization surgical biopsy to determine whether the calcification cluster is benign or malignant. However, a needle localization surgical biopsy is an invasive examination. This invasive examination leaves scars, causes pain, and makes the patient feel uncomfortable and unwilling to receive an immediate biopsy, resulting in a delay in treatment time. The researcher cooperated with a medical radiologist to analyze calcification clusters and lesions, employing a mammogram using a multi-architecture deep learning algorithm to solve these problems. The features of the location of the cluster and its benign or malignant status are collected from the needle localization surgical biopsy images and medical order and are used as the target training data in this study. This study adopts the steps of a radiologist examination. First, VGG16 is used to locate calcification clusters on the mammogram, and then the Mask R-CNN model is used to find micro-calcifications in the cluster to remove background interference. Finally, an Inception V3 model is used to analyze whether the calcification cluster is benign or malignant. The prediction precision rates of VGG16, Mask R-CNN, and Inception V3 in this study are 93.63%, 99.76%, and 88.89%, respectively, proving that they can effectively assist radiologists and help patients avoid undergoing a needle localization surgical biopsy.